class RN18(Net):
'''
ResNet18 has a total of 18 layers:
Note that some parameters are predetermined. The parameters need to be specified are in ''.
For all ResNetBlock modules, the output sizes of stage 1 and stage 2 conv2D blocks equals to
1/4 of that of the final stage.
Conv1 - kernel:(3x3), pad:(1,1), stride:1, output: 'c1OutChannel'
Conv1 - kernel:(3x3), pad:(1,1), stride:2, output: 'c2OutChannel' # H and W reduced by half
RNB1 - skipMode:identity, output : 'rnb1OutChannel'
RNB2 - skipMode:identity, output : same as RNB1
RNB3 - skipMode:identity, output : same as RNB1
RNB4 - skipMode:conv, skipStride:2, output : 'rnb4OutChannel' # H and W reduced by half
RNB5 - skipMode:identity, output : same as RNB4
pool - average pooling of RNB5 per channel, reducing output to 'rnb4OutChannel', need to specify
'pSize', which is used to specify stride and kernel size
fc - outChannel: 'classNum'
softmax - final classification layer
'''
def __init__(self, para):
Net.__init__(self, para)
convPara1 = {
'instanceName': 'RN18' + '_Conv1',
'padding': True,
'padShape': (1, 1),
'stride': 1,
'outChannel': para['c1OutChannel'],
'kernelShape': (3, 3),
'bias': False
}
self.conv1 = Conv2D(convPara1)
self.norm1 = Normalize({'instanceName': 'RN18' + '_Norm1'})
self.scale1 = Scale({'instanceName': 'RN18' + '_Scale1'})
self.activation1 = Activation({
'instanceName': 'RN18' + '_Activation1',
'activationType': 'ReLU'
})
self.layerList.append(self.conv1)
self.layerList.append(self.norm1)
self.layerList.append(self.scale1)
self.layerList.append(self.activation1)
convPara2 = {
'instanceName': 'RN18' + '_Conv2',
'padding': True,
'padShape': (1, 1),
'stride': 2,
'outChannel': para['c2OutChannel'],
'kernelShape': (3, 3),
'bias': False
}
self.conv2 = Conv2D(convPara2)
self.norm2 = Normalize({'instanceName': 'RN18' + '_Norm2'})
self.scale2 = Scale({'instanceName': 'RN18' + '_Scale2'})
self.activation2 = Activation({
'instanceName': 'RN18' + '_Activation2',
'activationType': 'ReLU'
})
self.layerList.append(self.conv2)
self.layerList.append(self.norm2)
self.layerList.append(self.scale2)
self.layerList.append(self.activation2)
self.rnb1 = ResNetBlock({
'instanceName': 'RN18' + '_RNB1',
'skipMode': 'identity',
'skipStride': 0,
'stride1': 1,
'outChannel1': int(para['rnb1OutChannel'] / 4),
'outChannel2': int(para['rnb1OutChannel'] / 4),
'outChannel3': para['rnb1OutChannel'],
'activationType': 'ReLU'
})
self.layerList.append(self.rnb1)
self.rnb2 = ResNetBlock({
'instanceName': 'RN18' + '_RNB2',
'skipMode': 'identity',
'skipStride': 0,
'stride1': 1,
'outChannel1': int(para['rnb1OutChannel'] / 4),
'outChannel2': int(para['rnb1OutChannel'] / 4),
'outChannel3': para['rnb1OutChannel'],
'activationType': 'ReLU'
})
self.layerList.append(self.rnb2)
self.rnb3 = ResNetBlock({
'instanceName': 'RN18' + '_RNB3',
'skipMode': 'identity',
'skipStride': 0,
'stride1': 1,
'outChannel1': int(para['rnb1OutChannel'] / 4),
'outChannel2': int(para['rnb1OutChannel'] / 4),
'outChannel3': para['rnb1OutChannel'],
'activationType': 'ReLU'
})
self.layerList.append(self.rnb3)
self.rnb4 = ResNetBlock({
'instanceName': 'RN18' + '_RNB4',
'skipMode': 'conv',
'skipStride': 2,
'stride1': 2,
'outChannel1': int(para['rnb4OutChannel'] / 4),
'outChannel2': int(para['rnb4OutChannel'] / 4),
'outChannel3': para['rnb4OutChannel'],
'activationType': 'ReLU'
})
self.layerList.append(self.rnb4)
self.rnb5 = ResNetBlock({
'instanceName': 'RN18' + '_RNB5',
'skipMode': 'identity',
'skipStride': 1,
'stride1': 1,
'outChannel1': int(para['rnb4OutChannel'] / 4),
'outChannel2': int(para['rnb4OutChannel'] / 4),
'outChannel3': para['rnb4OutChannel'],
'activationType': 'ReLU'
})
self.layerList.append(self.rnb5)
self.pool1 = Pool({
'instanceName': 'RN18' + '_pool1',
'poolType': 'ave',
'stride': para['pSize'],
'kernelShape': (para['pSize'], para['pSize'])
})
self.layerList.append(self.pool1)
self.fc1 = FullyConnected({
'instanceName': 'RN18' + '_fc1',
'outChannel': para['classNum'],
'bias': True
})
self.layerList.append(self.fc1)
self.softmax = Softmax({'instanceName': 'RN18' + '_softmax'})
self.layerList.append(self.softmax)
self.bottomInterface = self.conv1
self.topInterface = self.softmax
self.softmax.setNet(self)
def stack(self, top, bottom):
self.top = top
self.bottom = bottom
self.conv1.stack(self.norm1, bottom)
self.norm1.stack(self.scale1, self.conv1)
self.scale1.stack(self.activation1, self.norm1)
self.activation1.stack(self.conv2, self.scale1)
self.conv2.stack(self.norm2, self.activation1)
self.norm2.stack(self.scale2, self.conv2)
self.scale2.stack(self.activation2, self.norm2)
self.activation2.stack(self.rnb1, self.scale2)
self.rnb1.stack(self.rnb2, self.activation2)
self.rnb2.stack(self.rnb3, self.rnb1)
self.rnb3.stack(self.rnb4, self.rnb2)
self.rnb4.stack(self.rnb5, self.rnb3)
self.rnb5.stack(self.pool1, self.rnb4)
self.pool1.stack(self.fc1, self.rnb5)
self.fc1.stack(self.softmax, self.pool1)
self.softmax.stack(top, self.fc1)
self.softmax.setSource(bottom)
class ResNetBlock(Subnet):
'''
On the main path, the first convolution block has (1x1) kernel, zero padding. The second
convolution block has (3x3) kernel, (1,1) padding and stride of 1. The third convolution
block has (1x1) kernel, zero padding and stride of 1. The skip path has either identity mapping
or a convolution block with (1x1) kernel and zero padding. The number of output channels is
the same as that of the third convolution block on the main path.
Parameters required:
'instanceName': name of the block
'skipMode': slect the operations on the skip path, 'conv' or 'identity'
'skipStride': stride of the convolution block on the skip path
'stride1': stride of the first convolution block on the main path
'outChannel1': number of output channel of the first convolution block on the main path
'outChannel2': number of output channel of the second convolution block on the main path
'outChannel3': number of output channel of the third convolution block on the main path
'activationType': activation function of the non-linear block, 'ReLU' or 'sigmoid'
'''
# 'conv' mode has a convolution block on the skip path. 'identity' mode is strict pass through.
skipModes = ['conv', 'identity']
def __init__(self, para):
Subnet.__init__(self, para)
self.layerList = []
self.fork = Fork2({'instanceName': para['instanceName'] + '_fork'})
self.layerList.append(self.fork)
self.skipMode = para['skipMode']
if self.skipMode == 'conv':
convPara4 = {
'instanceName': para['instanceName'] + '_skipConv1',
'padding': False,
'padShape': (0, 0),
'stride': para['skipStride'],
'outChannel': para['outChannel3'],
'kernelShape': (1, 1),
'bias': False
}
self.skipConv = Conv2D(convPara4)
self.skipNorm = Normalize(
{'instanceName': para['instanceName'] + '_skipNorm'})
self.skipScale = Scale(
{'instanceName': para['instanceName'] + '_skipScale'})
self.layerList.append(self.skipConv)
self.layerList.append(self.skipNorm)
self.layerList.append(self.skipScale)
convPara1 = {
'instanceName': para['instanceName'] + '_mainConv1',
'padding': False,
'padShape': (0, 0),
'stride': para['stride1'],
'outChannel': para['outChannel1'],
'kernelShape': (1, 1),
'bias': False
}
convPara2 = {
'instanceName': para['instanceName'] + '_mainConv2',
'padding': True,
'padShape': (1, 1),
'stride': 1,
'outChannel': para['outChannel2'],
'kernelShape': (3, 3),
'bias': False
}
convPara3 = {
'instanceName': para['instanceName'] + '_mainConv3',
'padding': False,
'padShape': (0, 0),
'stride': 1,
'outChannel': para['outChannel3'],
'kernelShape': (1, 1),
'bias': False
}
self.mainConv1 = Conv2D(convPara1)
self.mainNorm1 = Normalize(
{'instanceName': para['instanceName'] + '_mainNorm1'})
self.mainScale1 = Scale(
{'instanceName': para['instanceName'] + '_mainScale1'})
self.mainActivation1 = Activation({
'instanceName':
para['instanceName'] + '_mainReLU1',
'activationType':
para['activationType']
})
self.layerList.append(self.mainConv1)
self.layerList.append(self.mainNorm1)
self.layerList.append(self.mainScale1)
self.layerList.append(self.mainActivation1)
self.mainConv2 = Conv2D(convPara2)
self.mainNorm2 = Normalize(
{'instanceName': para['instanceName'] + '_mainNorm2'})
self.mainScale2 = Scale(
{'instanceName': para['instanceName'] + '_mainScale2'})
self.mainActivation2 = Activation({
'instanceName':
para['instanceName'] + '_mainReLU2',
'activationType':
para['activationType']
})
self.layerList.append(self.mainConv2)
self.layerList.append(self.mainNorm2)
self.layerList.append(self.mainScale2)
self.layerList.append(self.mainActivation2)
self.mainConv3 = Conv2D(convPara3)
self.mainNorm3 = Normalize(
{'instanceName': para['instanceName'] + '_mainNorm3'})
self.mainScale3 = Scale(
{'instanceName': para['instanceName'] + '_mainScale3'})
self.layerList.append(self.mainConv3)
self.layerList.append(self.mainNorm3)
self.layerList.append(self.mainScale3)
self.sum = Sum2({'instanceName': para['instanceName'] + '_sum'})
self.activation3 = Activation({
'instanceName': para['instanceName'] + '_outReLU3',
'activationType': para['activationType']
})
self.layerList.append(self.sum)
self.layerList.append(self.activation3)
self.bottomInterface = self.fork
self.topInterface = self.activation3
def stack(self, top, bottom):
self.top = top
self.bottom = bottom
if self.skipMode == 'conv':
self.fork.fork(self.skipConv, self.mainConv1, bottom)
self.skipConv.stack(self.skipNorm, self.fork.skip)
self.skipNorm.stack(self.skipScale, self.skipConv)
self.skipScale.stack(self.sum.skip, self.skipNorm)
else:
self.fork.fork(self.sum.skip, self.mainConv1, bottom)
# main path
self.mainConv1.stack(self.mainNorm1, self.fork.main)
self.mainNorm1.stack(self.mainScale1, self.mainConv1)
self.mainScale1.stack(self.mainActivation1, self.mainNorm1)
self.mainActivation1.stack(self.mainConv2, self.mainScale1)
self.mainConv2.stack(self.mainNorm2, self.mainActivation1)
self.mainNorm2.stack(self.mainScale2, self.mainConv2)
self.mainScale2.stack(self.mainActivation2, self.mainNorm2)
self.mainActivation2.stack(self.mainConv3, self.mainScale2)
self.mainConv3.stack(self.mainNorm3, self.mainActivation2)
self.mainNorm3.stack(self.mainScale3, self.mainConv3)
self.mainScale3.stack(self.sum.main, self.mainNorm3)
# sum
if self.skipMode == 'conv':
self.sum.sum(self.activation3, self.skipScale, self.mainScale3)
else:
self.sum.sum(self.activation3, self.fork.skip, self.mainScale3)
self.activation3.stack(top, self.sum)
